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On the use of machine translation and topic-modeling to analyze non-parallel multilingual corpora: A case study in the history of philosophy of science ...
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On the use of machine translation and topic-modeling to analyze non-parallel multilingual corpora: A case study in the history of philosophy of science ...
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Developing and implementing an English-Spanish literary parallel audio-textual corpus for data-driven ESL learning
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In: DELTA: Documentação e Estudos em Linguística Teórica e Aplicada; v. 37 n. 1 (2021) ; 1678-460X ; 0102-4450 (2022)
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Parallel Corpora Preparation for English-Amharic Machine Translation
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In: IWANN 2021 - International Work on Artificial Neural Networks, Conference Springer LNCS proceedings ; https://hal.inria.fr/hal-03272258 ; IWANN 2021 - International Work on Artificial Neural Networks, Conference Springer LNCS proceedings, Jun 2021, Online, Spain (2021)
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CENTRAL KURDISH MACHINE TRANSLATION: FIRST LARGE SCALE PARALLEL CORPUS AND EXPERIMENTS
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In: https://hal.archives-ouvertes.fr/hal-03263105 ; 2021 (2021)
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Constructional equivalence in the Indonesian translations of ROB and STEAL ...
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Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
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In: Applied Sciences ; Volume 11 ; Issue 16 (2021)
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Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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Abstract:
In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over 90% for sentiment (3-class) classification and almost 80% for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT.
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Keyword:
BERT; chatbot; emotion recognition; fastText; human-machine interaction; machine translation; parallel text corpus; Polish language; sentiment recognition
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URL: https://doi.org/10.3390/app112110146
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A Systematic Literature Review of Lexical Analyzer Implementation Techniques in Compiler Design ...
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A Systematic Literature Review of Lexical Analyzer Implementation Techniques in Compiler Design ...
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Le particelle razve e neuželi alla luce del Corpus parallelo russo-italiano
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Noseda, Valentina (orcid:0000-0002-5148-1241). - : Aracne, 2021. : country:ITA, 2021. : place:Roma, 2021
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Explicitation and implicitation in translation: combining comparable and parallel corpus methodologies
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Explicitation and implicitation in translation: Combining comparable and parallel corpus methodologies
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